Last updated: 2019-06-22

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#Translation from HTSeq raw counts -> Count Matrix I have 86 TCGA patients with whole-genome sequencing data and RNAseq data as well as 99 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.

FOLDER <- "/Users/parajago/Research-Local/RNAseq-Local/Inputs/NigerianTCGA_quants-proteincoding"
sampleFiles <- grep("htseq.counts",list.files(FOLDER),value=TRUE)

#Differential gene expression setup based on race (b/w/other)
sampleConditionrace <- sampleFiles
countVar2=1
for (sample in sampleConditionrace){
  if (stri_detect_fixed(sample,"LIB")==TRUE){
    sampleConditionrace[countVar2] <- "Nigerian"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"black")==TRUE){
    sampleConditionrace[countVar2] <- "TCGA_black"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"white")==TRUE){
    sampleConditionrace[countVar2] <- "TCGA_white"
    countVar2=countVar2+1
  } else{
    sampleConditionrace[countVar2] <- "TCGA_other"
    countVar2=countVar2+1
  }
}

#Condition based on PAM50 subtype 
sampleConditionPAM50 <- sampleFiles
countVar2=1
for (sample in sampleConditionPAM50){
  if (stri_detect_fixed(sample,"Her2")==TRUE){
    sampleConditionPAM50[countVar2] <- "Her2"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"Basal")==TRUE){
    sampleConditionPAM50[countVar2] <- "Basal"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"LumA")==TRUE){
    sampleConditionPAM50[countVar2] <- "LumA"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"LumB")==TRUE){
    sampleConditionPAM50[countVar2] <- "LumB"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"PAMNL")==TRUE){
    sampleConditionPAM50[countVar2] <- "Normal"
    countVar2=countVar2+1
  } else{
    sampleConditionPAM50[countVar2] <- "PAM_other"
    countVar2=countVar2+1
  }
}

#Condition based on batch (relevant to the Nigerian patients only; no difference in batch for the TCGA patients)
batchval <- sampleFiles
countVar2=1
for (sample in batchval){
  if (stri_detect_fixed(sample,"batch1")==TRUE){
    batchval[countVar2] <- "batch1"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch23")==TRUE){
    batchval[countVar2] <- "batch23"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch4")==TRUE){
    batchval[countVar2] <- "batch4"
    countVar2=countVar2+1
  } else if (stri_detect_fixed(sample,"batch5")==TRUE){
    batchval[countVar2] <- "batch5"
    countVar2=countVar2+1
  } else{
    batchval[countVar2] <- "batchT"
    countVar2=countVar2+1
  }
}

table(sampleConditionrace, sampleConditionPAM50)
                   sampleConditionPAM50
sampleConditionrace Basal Her2 LumA LumB Normal PAM_other
         Nigerian      32   26   16   18      7         0
         TCGA_black    25    0    4    4      0         0
         TCGA_other     0    0    0    0      0        14
         TCGA_white    17    5    8    9      0         0
sampleTable2 <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
                          fileName=sampleFiles,
                          condition1=sampleConditionrace,
                          condition2=sampleConditionPAM50,
                          batch=batchval)

sampleTable2$sampleCondition <- paste(sampleTable2$condition1, sampleTable2$condition2, sep=".")

ddsHTSeqMF <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable2,
                                       directory=FOLDER,
                                       design=~sampleCondition)

ddsHTSeqMF <- ddsHTSeqMF[rowSums(counts(ddsHTSeqMF)) > 0, ] #Pre-filtering the dataset by removing the rows without any  information about gene expression

#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis.

##Data visualization

countmatrix <- assay(ddsHTSeqMF) #Raw counts organized into matrix format from individual files
countmatrix2 <- log2(countmatrix + 1) #Basic transformation of the count data 

plot(density(countmatrix2[,1]),lwd=3,ylim=c(0,.30), main="Density of counts with log2[count]+1 transformation ONLY") 
for(i in 1:185){lines(density(countmatrix2[,i]),lwd=3)} #This demonstrates that there is a difference in distributions between the Nigerian and TCGA data with basic log transformation normalization 

norm_countmatrix <- as.matrix(countmatrix2) 
norm_countmatrix = normalize.quantiles(norm_countmatrix)
plot(density(norm_countmatrix[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization")
for(i in 1:184){lines(density(norm_countmatrix[,i]),lwd=3)} #This demonstrates the effect of comparative quantile normalization

colnames (norm_countmatrix) <- colnames (countmatrix2)
rownames (norm_countmatrix) <- rownames (countmatrix2)

norm_countmatrix <- as.data.frame(norm_countmatrix)
countmatrixNigerian <- dplyr::select(norm_countmatrix, contains("LIB"))
plot(density(countmatrixNigerian[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - Nigerian")
for(i in 1:98){lines(density(countmatrixNigerian[,i]),lwd=3)} #This demonstrates the result of the normalized Nigerian counts separately

tcgacolnames <- colnames(countmatrix)
tcgacolnames <- setdiff(tcgacolnames, colnames(countmatrixNigerian))
countmatrixTCGA <- norm_countmatrix[ , tcgacolnames]
plot(density(countmatrixTCGA[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - TCGA")
for(i in 1:85){lines(density(countmatrixTCGA[,i]),lwd=3)} #This demonstrates the result of the normalized TCGA counts separately

norm_countmatrix <- as.data.frame(norm_countmatrix)
t_norm_countmatrix <- t(norm_countmatrix)

t_norm_countmatrix <- cbind (t_norm_countmatrix, sampleTable2) #This binds the characteristics of the original patients to the quantile normalized counts. CBinding was checked to make sure that patients were correctly aligned to characteristics. 

quant.pca <- prcomp(t_norm_countmatrix[,1:19745])
autoplot(quant.pca, data=t_norm_countmatrix, colour='sampleCondition', main="PCA of quantile normalization results prior to DE analysis")

In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering and % explanations relative to VSD normalization. Only ~20% of the distribution of the data set is explained by the PCA1, 2 variables.

##Differential expression setup

annotation <- as.data.frame(row.names(countmatrix))
colnames(annotation) <- c("GeneID")
annotation$temp <- gsub("[.].+", "", annotation[,1])

annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SYMBOL",
                     keytype="GENEID",           
                     multiVals="first")

annotation$chr <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="SEQNAME",
                     keytype="GENEID",           
                     multiVals="first")

annotation$locstart <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="GENESEQSTART",
                     keytype="GENEID",
                     multiVals="first")

annotation$locend <- mapIds(EnsDb.Hsapiens.v75,
                     keys=annotation$temp,
                     column="GENESEQEND",
                     keytype="GENEID",
                     multiVals="first")
annotation$temp <- NULL

design <- t_norm_countmatrix
design <- design %>% dplyr::select(sampleCondition)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - Basal

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.Basal", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_white.Basal", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.Basal <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_white.Basal <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.Basal+designNTW$TCGA_white.Basal)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_white")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 14750    49
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAwhiteBasal = Nigerian.Basal-TCGA_white.Basal, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAwhiteBasal
Down                       2673
NotSig                    10295
Up                         1782
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between basal breast cancers \nin Nigerian and \nTCGA white breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between basal \nbreast cancers in Nigerian and \nTCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-Basal-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - Basal

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.Basal", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_black.Basal", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.Basal <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_black.Basal <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.Basal+designNTW$TCGA_black.Basal)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_black")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 16496    57
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAblackBasal = Nigerian.Basal-TCGA_black.Basal, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAblackBasal
Down                       2491
NotSig                    11878
Up                         2127
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between basal breast cancers \nin Nigerian and \nTCGA black breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between basal \nbreast cancers in Nigerian and \nTCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAblack-Basal-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - Her2

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.Her2", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_white.Her2", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.Her2 <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_white.Her2 <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.Her2+designNTW$TCGA_white.Her2)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_white")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13871    31
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAwhiteHer2 = Nigerian.Her2-TCGA_white.Her2, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAwhiteHer2
Down                       680
NotSig                   12888
Up                         303
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between Her2 breast cancers \nin Nigerian and \nTCGA white breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between Her2 \nbreast cancers in Nigerian and \nTCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-Her2-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - LumA

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.LumA", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_white.LumA", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.LumA <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_white.LumA <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.LumA+designNTW$TCGA_white.LumA)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_white")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13584    24
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAwhiteLumA= Nigerian.LumA-TCGA_white.LumA, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAwhiteLumA
Down                       998
NotSig                   12007
Up                         579
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between LumA breast cancers \nin Nigerian and \nTCGA white breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between LumA \nbreast cancers in Nigerian and \nTCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-LumA-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - LumA

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.LumA", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_black.LumA", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.LumA <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_black.LumA <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.LumA+designNTW$TCGA_black.LumA)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_black")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13461    20
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAblackLumA= Nigerian.LumA-TCGA_black.LumA, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAblackLumA
Down                        95
NotSig                   13255
Up                         111
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between LumA breast cancers \nin Nigerian and \nTCGA black breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between LumA \nbreast cancers in Nigerian and \nTCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAblack-LumA-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - LumB

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.LumB", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_white.LumB", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.LumB <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_white.LumB <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.LumB+designNTW$TCGA_white.LumB)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_white")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13414    27
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAwhiteLumB= Nigerian.LumB-TCGA_white.LumB, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAwhiteLumB
Down                       769
NotSig                   11973
Up                         672
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between LumB breast cancers \nin Nigerian and \nTCGA white breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between LumB \nbreast cancers in Nigerian and \nTCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-LumB-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - LumB

designNTW <- design
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="Nigerian.LumB", 0, as.character(designNTW$sampleCondition))
designNTW$sampleCondition <- ifelse (designNTW$sampleCondition=="TCGA_black.LumB", 1, as.character(designNTW$sampleCondition))

designNTW$sampleCondition <- ifelse (designNTW$sampleCondition==0 | designNTW$sampleCondition==1, designNTW$sampleCondition, NA)

designNTW <- designNTW %>% subset(is.na(sampleCondition)==FALSE)

designNTW$Nigerian.LumB <- ifelse (designNTW$sampleCondition==0, 1, 0)
designNTW$TCGA_black.LumB <- ifelse (designNTW$sampleCondition==1, 1, 0)

designNTW$sampleCondition <- NULL

mm <- model.matrix(~0+designNTW$Nigerian.LumB+designNTW$TCGA_black.LumB)

quantids <- rownames(designNTW)
rownames(mm) <- quantids
colnames(mm) <- c("Nigerian", "TCGA_black")

quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)

d0 <- DGEList(counts=quantdata, genes=annotation)

cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # Number of genes after taking out low expressed genes
[1] 13373    22
v=voom(d,designNTW,plot=T, normalize="quantile")

contr.matrix <- makeContrasts(NigerianvsTCGAblackLumB= Nigerian.LumB-TCGA_black.LumB, levels=colnames(designNTW))

fit <- lmFit(v, designNTW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
       NigerianvsTCGAblackLumB
Down                        71
NotSig                   13210
Up                          92
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential\n gene expression between LumB breast cancers \nin Nigerian and \nTCGA black breast cancer patients\n quantile corrected")

qvals<-p.adjust(fit$p.value[,1], method='fdr')

df_limma <- data_frame(log2FoldChange = fit$coefficients[,1], 
                       pval = fit$p.value[,1],
                       padj = qvals,
                       anno = fit$genes)

with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between LumB \nbreast cancers in Nigerian and \nTCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))

df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)

top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAblack-LumB-DE.csv", row.names = FALSE)

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details. 


sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] Glimma_1.12.0               RColorBrewer_1.1-2         
 [3] preprocessCore_1.46.0       ashr_2.2-32                
 [5] ggfortify_0.4.7             calibrate_1.7.2            
 [7] MASS_7.3-51.4               sva_3.32.1                 
 [9] mgcv_1.8-28                 nlme_3.1-140               
[11] EnsDb.Hsapiens.v75_2.99.0   ensembldb_2.8.0            
[13] AnnotationFilter_1.8.0      GenomicFeatures_1.36.1     
[15] hexbin_1.27.3               stringi_1.4.3              
[17] dplyr_0.8.1                 affy_1.62.0                
[19] checkmate_1.9.3             pathview_1.24.0            
[21] org.Hs.eg.db_3.8.2          AnnotationDbi_1.46.0       
[23] clusterProfiler_3.12.0      pheatmap_1.0.12            
[25] genefilter_1.66.0           vsn_3.52.0                 
[27] RUVSeq_1.18.0               EDASeq_2.18.0              
[29] ShortRead_1.42.0            GenomicAlignments_1.20.0   
[31] Rsamtools_2.0.0             Biostrings_2.52.0          
[33] XVector_0.24.0              DESeq2_1.24.0              
[35] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[37] BiocParallel_1.18.0         matrixStats_0.54.0         
[39] Biobase_2.44.0              GenomicRanges_1.36.0       
[41] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[43] S4Vectors_0.22.0            BiocGenerics_0.30.0        
[45] edgeR_3.26.4                limma_3.40.2               
[47] ggbiplot_0.55               scales_1.0.0               
[49] plyr_1.8.4                  ggplot2_3.1.1              
[51] gplots_3.0.1.1             

loaded via a namespace (and not attached):
  [1] R.utils_2.8.0          tidyselect_0.2.5       RSQLite_2.1.1         
  [4] htmlwidgets_1.3        DESeq_1.36.0           munsell_0.5.0         
  [7] codetools_0.2-16       withr_2.1.2            colorspace_1.4-1      
 [10] GOSemSim_2.10.0        knitr_1.23             rstudioapi_0.10       
 [13] pscl_1.5.2             DOSE_3.10.1            labeling_0.3          
 [16] git2r_0.25.2           KEGGgraph_1.44.0       urltools_1.7.3        
 [19] GenomeInfoDbData_1.2.1 mixsqp_0.1-97          hwriter_1.3.2         
 [22] polyclip_1.10-0        bit64_0.9-7            farver_1.1.0          
 [25] rprojroot_1.3-2        xfun_0.7               doParallel_1.0.14     
 [28] R6_2.4.0               locfit_1.5-9.1         bitops_1.0-6          
 [31] fgsea_1.10.0           gridGraphics_0.4-1     assertthat_0.2.1      
 [34] ggraph_1.0.2           nnet_7.3-12            enrichplot_1.4.0      
 [37] gtable_0.3.0           workflowr_1.4.0        rlang_0.3.4           
 [40] splines_3.6.0          rtracklayer_1.44.0     lazyeval_0.2.2        
 [43] acepack_1.4.1          europepmc_0.3          BiocManager_1.30.4    
 [46] yaml_2.2.0             reshape2_1.4.3         backports_1.1.4       
 [49] qvalue_2.16.0          Hmisc_4.2-0            tools_3.6.0           
 [52] ggplotify_0.0.3        affyio_1.54.0          ggridges_0.5.1        
 [55] Rcpp_1.0.1             base64enc_0.1-3        progress_1.2.2        
 [58] zlibbioc_1.30.0        purrr_0.3.2            RCurl_1.95-4.12       
 [61] prettyunits_1.0.2      rpart_4.1-15           viridis_0.5.1         
 [64] cowplot_0.9.4          ggrepel_0.8.1          cluster_2.0.9         
 [67] fs_1.3.1               magrittr_1.5           data.table_1.12.2     
 [70] DO.db_2.9              triebeard_0.3.0        truncnorm_1.0-8       
 [73] SQUAREM_2017.10-1      ProtGenerics_1.16.0    aroma.light_3.14.0    
 [76] hms_0.4.2              evaluate_0.14          xtable_1.8-4          
 [79] XML_3.98-1.20          gridExtra_2.3          compiler_3.6.0        
 [82] biomaRt_2.40.0         tibble_2.1.3           KernSmooth_2.23-15    
 [85] crayon_1.3.4           R.oo_1.22.0            htmltools_0.3.6       
 [88] Formula_1.2-3          tidyr_0.8.3            geneplotter_1.62.0    
 [91] DBI_1.0.0              tweenr_1.0.1           Matrix_1.2-17         
 [94] R.methodsS3_1.7.1      gdata_2.18.0           igraph_1.2.4.1        
 [97] pkgconfig_2.0.2        rvcheck_0.1.3          foreign_0.8-71        
[100] foreach_1.4.4          xml2_1.2.0             annotate_1.62.0       
[103] stringr_1.4.0          digest_0.6.19          graph_1.62.0          
[106] rmarkdown_1.13         fastmatch_1.1-0        htmlTable_1.13.1      
[109] curl_3.3               gtools_3.8.1           jsonlite_1.6          
[112] viridisLite_0.3.0      pillar_1.4.1           lattice_0.20-38       
[115] KEGGREST_1.24.0        httr_1.4.0             survival_2.44-1.1     
[118] GO.db_3.8.2            glue_1.3.1             UpSetR_1.4.0          
[121] iterators_1.0.10       png_0.1-7              bit_1.1-14            
[124] Rgraphviz_2.28.0       ggforce_0.2.2          blob_1.1.1            
[127] latticeExtra_0.6-28    caTools_1.17.1.2       memoise_1.1.0